Diseases such as cancer, heart disease, autoimmune disease, neurodegenerative disorders and infectious disease are leading causes of death in the United States. For example, the American Cancer Society projects 1,334,100 new cases of cancer occurred in 2003 in the U.S. with about 556,500 deaths. The cost of such diseases also has a major economic impact on the United States of America. The National Institute of Health projects that cancer cost the U.S. $171.6 billion in 2002. Despite the enormous costs involved, treatment of diseases such as cancer is typically developed through consensus-based medicine using little or no data specific to individual patients. The use of such methods to care for patients having such diseases leads to inefficient and often ineffective health care.
Small changes in treatment of such diseases cannot only have a major impact on the health and well-being of society, it also has a monetary value. For example, in 2002, the cost of cancer for Florida alone was estimated to be $12.3 billion. See, for example, “2003 Cancer Facts and Figures,” American Cancer Society, 2004. This includes direct medical costs, cost of lost productivity due to illness, and cost of lost productivity due to premature death. Decreasing the cost of cancer in Florida by just two percent, or $246 million, would be significant in the overall economic impact.
In the case of cancer, physicians are currently unable to understand a patient's specific type of cancer beyond the visual microscopic analysis of cells. Cancer researchers have studied the molecular mechanisms behind these visual changes in behavior for years, but have not had the capabilities to understand these changes in individual patients.
A survey of the literature shows that there is a growing appreciation for the information that molecular profiling can provide. For example Yeoh, E-J. et al., 2002, Cancer Cell 1:133-143 used gene microarray technology to determine the molecular signatures for seven different subtypes of pediatric leukemia. For some leukemia subgroups, a subset of the identified genes could predict whether patients were at high risk of relapse. Hofmann et al., 2002, The Lancet, 359:481-486 used gene expression signatures from HuGeneFL to identify a correlation between gene expression profiles of bone marrow samples of Ph+ ALL patients, and resistance to the drug Imatinib. Armstrong et al., 2002, Nature Genetics 30:41-47, developed clustering algorithms using microarray data, and employed them to show that lymphoblastic leukemia, with mixed lineage translocations (MLL), has a prognosis and gene expression signature that is distinct from AML or ALL thereby showing that molecular signatures can serve as a basis for identification of unique diseases. Ramaswamy et al., 2003, Nature Genetics 33:49-54, used various microarray platforms to show that, across multiple tumor types, molecular signatures can be used to predict metastasis and poor clinical outcome. Oestreicher et al., 2001, Pharmacogenomics J. 1:272-87, used microarray technology to perform a genome-wide scan of multiple psoriasis patients and showed 159 genes associated with the disease. A longitudinal study of two different treatment regimens showed that, for a subset of the 159 genes, transcript levels changed significantly in those who responded and, in some cases, preceded clinical improvement.
Thus, while there is a growing body of molecular profiling information, such information is typically not used to treat individual patients. Rather, a consensus based approach in which established treatment regimens are followed is the norm. Accordingly, given the above background, what is needed in the art are systems and methods that will allow physicians and patients to harness the capabilities of molecular medicine and develop evidence-based therapies for patients.
Discussion or citation of a reference herein will not be construed as an admission that such reference is prior art to the present invention.